Video on demand (VoD) is a system in which users may select and watch videos according to their own time preferences (e.g., on demand). Many users currently make use of VoD at their desktop or laptop watching videos provided by various content providers via the Internet. However, as the capability of mobile wireless devices increases, users will increasingly use their mobile devices to view VoDs. It is projected that in the near future, VoD will constitute a major portion (e.g., greater than 70%) of mobile traffic.
In order to deliver video and other services (e.g., web pages) to mobile devices, the transmission point (TP) (e.g., a base station transceiver (BST)) may schedule various VoD flows and best effort flows (e.g., a mechanism for delivering other content such as web pages) to utilize the available transmission bandwidth. Some schedulers may use static partitioning of the bandwidth with separate schedulers for VoD flows and for best effort flows. Additionally, some schedulers utilize a regular proportional fairness (PF) utility plus a barrier function of the video playback buffer occupancy. Barrier functions impact the scheduling decisions only when the buffer occupancy is below a preset threshold, which may increase the fairness among user buffers as it increases and vice versa. Some of the schedulers further utilize an empirical weighting factor to scale the barrier function to impose fairness in terms of the total rebuffering time. Furthermore, these schedulers may also result in variations in quality across the system which may cause some users to have a poor quality of experience (QoE) when viewing videos on their wireless devices. Also, resource partitioning and scheduling VoD flows separately as provided by these schedules lacks flexibility to adjust to changing traffic conditions and compromises Frequency Selective Scheduling (FSS) gains. Rebuffering time may be the most critical attribute to a VoD user's QoE and service outage criteria are typically based on the percentage of total rebuffering time the user experiences. However, these schedulers may result in an inefficient use of bandwidth resources and provide a less than desirable QoE for the user.